1. GOES-R AWG Aviation Team: SO2 Detection
Presented By: Mike Pavolonis
NOAA/NESDIS/STAR

2. Outline Aviation Team Executive Summary Algorithm Description
ADEB and IV&V Response Summary
Requirements Specification Evolution
Validation Strategy
Validation Results
Summary

3. Aviation Team Co-Chairs: Ken Pryor and Wayne Feltz Aviation Team
K. Bedka, J. Brunner
J. Mecikalski, M. Pavolonis, B. Pierce
W. Smith, Jr., A. Wimmers, J. Sieglaff
Others
Walter Wolf (AIT Lead)
Shanna Sampson (Algorithm Integration)
Zhaohui Zhang (Algorithm Integration)
Thanks to many others as well, S. Wanzong, D. Hillger, etc. 3 3

4. Executive Summary
The ABI SO2 detection algorithm produces a yes/no SO2 flag
Software Version 4 was delivered in January (2011). The CUTR was completed in January (2011). ATBD (100%) and test datasets are scheduled to be delivered in June (2011).
The SO2 detection algorithm utilizes spectral and spatial information to identify SO2 clouds. Only infrared measurements are used, so the algorithm methodology is day/night consistent.
Validation Datasets: Hyperspectral ultra-violet based retrievals of SO2 total column loading from the Ozone Monitoring Instrument (OMI).
Validation studies indicate that the SO2 loading algorithm is meeting the 100% accuracy specification.
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5. Algorithm Description
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6. Algorithm Summary
Input Datasets: 1). ABI channels 8, 10, 11, 14, and 15, 2). Satellite viewing angle, 3). Clear sky radiances and transmittances, 4). Cloud frequency of occurrence LUT
-ratios are calculated to help identify cloud microphysical “anomalies” possibly associated with SO2 absorption.
Cloud objects are constructed from potential SO2 pixels.
Spectral and spatial cloud object statistics are used to determine if all of the pixels that compose a cloud object contain SO2 or do not contain SO2.
A flag indicating the approximate SO2 loading is also produced and stored in the quality flags.
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7. Algorithm Channel Selection
The ABI algorithm utilizes SO2 absorption lines located in the channel 10 (7.3 m) and channel 11 (8.5 m) bands in combination with the lack of SO2 absorption in the 6.2, 11, and 12 m bands to detect SO2 induced spectral anomalies.
ABI channel 11 coverage
ABI channel 10 coverage

8. Spectral Information
As in Pavolonis (2010), effective absorption optical depth ratios (-ratios) are used to infer cloud microphysical information.
Cloud microphysical anomalies (large deviations from theoretical values) are then used to identify potential SO2 contaminated pixels.
Spectral ratio of effective absorption optical depth
Spectral ratio of scaled extinction coefficients
This relationship provides a direct link to theoretical size distributions from the measurements

9. Spectral Information
Water Cloud
Ice Cloud
SO2 Cloud
Compare the spectral signature of an SO2 cloud, an ice cloud ,and a liquid water cloud.

10. Spectral Information (8.5, 11), (12, 11) SO2 spectral signature
The SO2 cloud is characterized by the simultaneous occurrence of large (12, 11) and large (8.5, 11).
Ice clouds tend to have smaller (8.5, 11) relative to SO2 clouds.
SO2 clouds tend to have larger (12, 11) relative to liquid water clouds when hydrometeors are co-located with the SO2.
SO2
Liquid Water
Ice

11. Spectral Information
The SO2 cloud is characterized by the simultaneous occurrence of large (7.3, 11) and large (8.5, 11).
Ice clouds tend to have smaller (8.5, 11) and (7.3, 11) relative to SO2 clouds.
Liquid water clouds are often present well below the peak of the 7.3 m weighting function, and hence does not noticeably impact the 7.3 m radiance.
(8.5, 11), (7.3, 11) SO2 spectral signature
SO2
Liquid Water
Ice

12. SO2 Detection Processing Schematic
INPUT: ABI and ancillary data
Compute cloud emissivities and -ratios
Determine which pixels meet cloud object membership criteria
Fifth Algorithm Summary Slide
Construct cloud objects and compute cloud object statistics
Determine which cloud objects are SO2 clouds
Output SO2 mask

13. Cordon Caulle (Chile), June 06 2011 – 17:00
Example Output
Cordon Caulle (Chile), June – 17:00

14. The GOES-R and OMI products are in good agreement (more on this later)
Example Output
Grimsvotn, May 22, :00 UTC
OMI SO2 Product
GOES-R SO2 Product
Iceland
Iceland
The GOES-R and OMI products are in good agreement (more on this later)

15. Algorithm Changes from 80% to 100%
Improved SO2 detection in the absence of a strong 7.3 μm signal (8.5 μm signal only).
Improved SO2 detection when SO2 overlaps or is mixed with thick ice cloud (more work is still needed though)
Metadata output added
Quality flags standardized

16. ADEB and IV&V Response Summary
All ATBD errors and clarification requests have been addressed.
No substantive modifications to the approach were required.
State the qualifiers on your products. (if obvious , skip this)

17. Requirements and Product Qualifiers SO2 Detection
Name
User &
Priority
Geographic
Coverage
(G, H, C, M)
Vertical
Resolution
Horizontal
Mapping
Accuracy
Measurement
Range
Product Refresh
Rate/Coverage Time (Mode 3)
Rate/Coverage Time (Mode 4)
Vendor Allocated Ground Latency
Product Measurement Precision
SO2 Detection
GOES-R
Full Disk
Total Column
2 km
1 km
Binary yes/no detection from 10 to 700 Dobson Units (DU)
70% correct detection
Full disk: 60 min
Full disk: 5 min
806 sec
N/A
Name
User &
Priority
Geographic
Coverage
(G, H, C, M)
Temporal Coverage Qualifiers
Product Extent Qualifier
Cloud Cover Conditions Qualifier
Product Statistics Qualifier
SO2 Detection
GOES-R
Full Disk
Day and night
Quantitative out to at least 70 degrees LZA and qualitative at larger LZA
Clear conditions down to feature of interest associated with threshold accuracy
Over specified geographic area

18. Validation Approach
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19. Validation Approach
OMI is a Dutch-Finnish instrument on board the Aura satellite in NASA’s A-Train.
OMI uses measurements of backscattered solar UV radiation to detect SO2. OMI can detect SO2 at levels less than 1 DU.
Although OMI is very sensitive to SO2, it only views a given area of earth once daily.
The MODIS instrument on the Aqua satellite (also in the A-Train) observes the same area as OMI, which allows us to study any SO2 clouds observed by OMI.
The Ozone Monitoring Instrument (OMI) is used as “truth”
The ABI SO2 mask is validated as a function of OMI SO2 loading 19

20. Validation Approach Spurious data Before filtering After filtering
OMI SO2 Quality Flag (QF) from the OMI data set is used to filter out poor quality SO2 loading retrievals
Spurious data
Before filtering
After filtering

21. Validation Approach
The OMI SO2 loading product and the GOES-R proxy data (MODIS or SEVIRI) are matched up in time and re-mapped to the same grid to allow for quantitative comparisons.

22. Validation Results
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23. SO2 Validation Accuracy
The ABI SO2 mask is validated as a function of OMI SO2 loading.
Only scenes that contained SO2 clouds were used so this analysis reflects how the algorithm will perform in relevant situations
The accuracy is 79% when the OMI SO2 indicates 10 DU or more of SO2.
Accuracy
Accuracy Requirement

24. SO2 Validation True Skill Score
While not required the true skill score was also evaluated.
The true skill score is 0.59 when the OMI SO2 indicates 10 DU or more of SO2.
The true skill score is 0.70 when the OMI SO2 indicates 14 DU or more of SO2.
True Skill Score
-The Heidke skill score is 0.79 at 10 DU.
-How well did the classifier separate the “yes” events from the “no” events

25. F&PS requirements – SO2
F&PS requirement: Product Measurement Range
F&PS requirement: Product Measurement Accuracy
Validation results
Binary yes/no detection from 10 to 700 Dobson Units (DU)
70% correct detection
79% correct detection when loading is 10 DU or greater (0.70 true skill score when loading is 14 DU or greater) 25

26. Summary
The ABI SO2 Detection algorithm provides a new capability to objectively detect SO2 clouds, which may be an aviation hazard and impact climate.
The GOES-R AWG SO2 algorithm meets all performance and latency requirements.
The improved spatial and temporal resolution of the ABI, along with a 7.3 um band that is better suited for SO2 detection, will likely lead to improved SO2 detection capabilities (relative to SEVIRI and MODIS).
The 100% ATBD and code will be delivered to the AIT this summer.
Prospects for future improvement: 1). Express results as a probability, not a mask. 2). Correct for high level ice and ash clouds using bands not sensitive to SO2 3). Improve quantitative estimates of SO2 loading and possibly height
GOES-R Risk Reduction will be used to make these improvements for an auto alert system